Abstract: Machine learning has become an indispensable tool across many areas of research and commercial applications. From text-to-speech for your phone to detect the Higgs boson, machine learning excels at extracting knowledge from large amounts of data. This talk will give a general introduction to machine learning, as well as introduce practical tools for you to apply machine learning in your research. We will focus on one particularly important subfield of machine learning, supervised learning. The goal of supervised learning is to "learn" a function that maps inputs x to an output y, by using a collection of training data consisting of input-output pairs. We will walk through formalizing a problem as a supervised machine learning problem, creating the necessary training data and applying and evaluating a machine learning algorithm. The talk should give you all the necessary background to start using machine learning yourself.
Bio: Andreas Mueller received his MS degree in Mathematics (Dipl.-Math.) in 2008 from the Department of Mathematics at the University of Bonn. In 2013, he finalized his Ph.D. thesis at the Institute for Computer Science at the University of Bonn. After working as a machine learning scientist at the Amazon Development Center Germany in Berlin for a year, he joined the Center for Data Science at the New York University at the end of 2014. In his current position as an assistant research engineer at the Center for Data Science, he works on open source tools for machine learning and data science. He is one of the core contributors of scikit-learn, a machine learning toolkit widely used in industry and academia, for several years, and has authored and contributed to a number of open source projects related to machine learning.
Thomas Fan is a Software Developer at Columbia University's Data Science Institute. He collaborates with the scikit-learn community to develop features, review code, and resolve issues. On his free time, Thomas contributes to skorch, a scikit-learn compatible neural network library that wraps PyTorch.